1. Federated Multimodal Big Data Storage & Analytics Platform for Additive Manufacturing
- Author
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Justin John Gambone, Joseph Vinciquerra, Paul Edward Cuddihy, Vijay S. Kumar, Laura Cerully Dial, Vipul K. Gupta, Kareem Sherif Aggour, Timothy Hanlon, and Jenny Weisenberg Williams
- Subjects
business.industry ,Process (engineering) ,Computer science ,010401 analytical chemistry ,Big data ,02 engineering and technology ,01 natural sciences ,Data science ,0104 chemical sciences ,Metadata ,Analytics ,020204 information systems ,Spark (mathematics) ,Computer data storage ,0202 electrical engineering, electronic engineering, information engineering ,User interface ,business - Abstract
Additive technologies are expected to revolutionize manufacturing across almost every industry, but there is a sizable gap between the current state of the technology and the maturity required for it to achieve widespread adoption. Advanced analytics are required to improve the reliability and repeatability of additive manufacturing, and those analytics require data. Large volumes of multimodal data are generated and used throughout the additive manufacturing lifecycle, from material design to part design and simulation, part printing to post-processing and inspection. To capture and link that diverse Big Data together, we have designed and developed a federated multimodal Big Data storage and analytics platform comprised of three tiers—a distributed polyglot data storage and analysis tier with different repositories for different data structures, a metadata knowledge graph tier for modeling the data and their relationships across the various repositories, and a user interface tier for visualizing, exploring and invoking analytics on the data. The platform has been used to integrate a collection of previously disparate steps to optimize the process parameters used to build a new additive material, enabling materials scientists and other non-software experts to capture, visualize and analyze the requisite data through a single user interface.
- Published
- 2019
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